# Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals

arXiv: https://arxiv.org/abs/2101.04824 · HTML: https://mlsanity.com/paper/2101.04824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents](https://mlsanity.com/paper/1812.09985/) (2018)

## Abstract (arXiv)

In this work, we present an energy-efficient distributed learning framework using low-resolution ADCs and coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. We also carry out a statistical analysis of the proposed DQA-LMS algorithm that includes a stability condition. Simulations assess the DQA-LMS algorithm against existing techniques for a distributed parameter estimation task where IoT devices operate in a peer-to-peer mode and demonstrate the effectiveness of the DQA-LMS algorithm.